Method and apparatus for monitoring controller performance using statistical process control

ABSTRACT

The present invention provides for a method and an apparatus for monitoring controller performance using statistical process control analysis. A manufacturing model is defined. A processing run of semiconductor devices is performed as defined by the manufacturing model and implemented by a process controller. A fault detection analysis is performed on the process controller. At least one control input signal generated by the process controller is updated. The apparatus of the present invention comprises: a processing controller; a processing tool coupled with the processing controller; a metrology tool interfaced with the processing tool; a control modification data calculation unit interfaced with the metrology and connected to the processing controller in a feedback manner; a predictor function interfaced with the processing controller; an statistical process control analysis unit interfaced with the predictor function and the processing tool; and a results versus prediction analysis unit interfaced with the statistical process control analysis unit and connected to the processing controller in a feedback manner.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates generally to semiconductor productsmanufacturing, and, more particularly, to a method and apparatus formonitoring controller performance using statistical process control.

2. Description of the Related Art

The technology explosion in the manufacturing industry has resulted inmany new and innovative manufacturing processes. Today's manufacturingprocesses, particularly semiconductor manufacturing processes, call fora large number of important steps. These process steps are usuallyvital, and therefore, require a number of inputs that are generally finetuned to maintain proper manufacturing control.

The manufacture of semiconductor devices requires a number of discreteprocess steps to create a packaged semiconductor device from rawsemiconductor material. The various processes, from the initial growthof the semiconductor material, the slicing of the semiconductor crystalinto individual wafers, the fabrication stages (etching, doping, ionimplanting, or the like), to the packaging and final testing of thecompleted device, are so different from one another and specialized thatthe processes may be performed in different manufacturing locations thatcontain different control schemes.

Among the important aspects in semiconductor device manufacturing areRTA control, chemical-mechanical planarization (CMP) control, andoverlay control. Overlay is one of several important steps in thephotolithography area of semiconductor manufacturing. Overlay controlinvolves measuring the misalignment between two successive patternedlayers on the surface of a semiconductor device. Generally, minimizationof misalignment errors is important to ensure that the multiple layersof the semiconductor devices are connected and functional. As technologyfacilitates smaller critical dimensions for semiconductor devices, theneed for reduced of misalignment errors increases dramatically.

Generally, photolithography engineers currently analyze the overlayerrors a few times a month. The results from the analysis of the overlayerrors are used to make updates to exposure tool settings manually.Generally, a manufacturing model is employed to control themanufacturing processes. Some of the problems associated with thecurrent methods include the fact that the exposure tool settings areonly updated a few times a month. Furthermore, currently the exposuretool updates are performed manually. Many times, errors in semiconductormanufacturing are not organized and reported to quality controlpersonal. Often, the manufacturing models themselves incur bias errorsthat could compromise manufacturing quality.

Generally, a set of processing steps is performed on a lot of wafers ona semiconductor manufacturing tool called an exposure tool or a stepper.The manufacturing tool communicates with a manufacturing framework or anetwork of processing modules. The manufacturing tool is generallyconnected to an equipment interface. The equipment interface isconnected to a machine interface to which the stepper is connected,thereby facilitating communications between the stepper and themanufacturing framework. The machine interface can generally be part ofan advanced process control (APC) system. The APC system initiates acontrol script based upon a manufacturing model, which can be a softwareprogram that automatically retrieves the data needed to execute amanufacturing process. Often, semiconductor devices are staged throughmultiple manufacturing tools for multiple processes, generating datarelating to the quality of the processed semiconductor devices. Manytimes, errors in semiconductor manufacturing are not organized andreported to quality control personal, which can result in reducedefficiency in manufacturing processes. Errors in the manufacturing modelthat is used to perform the manufacturing process, such as bias errors,often compromises the quality of manufactured products.

The present invention is directed to overcoming, or at least reducingthe effects of, one or more of the problems set forth above.

SUMMARY OF THE INVENTION

In one aspect of the present invention, a method is provided formonitoring controller performance using statistical process controlanalysis. A manufacturing model is defined. A processing run ofsemiconductor devices is performed as defined by the manufacturing modeland implemented by a process controller. A fault detection analysis isperformed on the process controller. At least one control input signalgenerated by the process controller is updated.

In another aspect of the present invention, an apparatus is provided formonitoring controller performance using statistical process controlanalysis. The apparatus of the present invention comprises: a processingtool coupled with the processing controller; a metrology tool interfacedwith the processing tool; a control modification data calculation unitinterfaced with the metrology and connected to the processing controllerin a feedback manner; a predictor function interfaced with theprocessing controller; an statistical process control analysis unitinterfaced with the predictor function and the processing tool; and aresults versus prediction analysis unit interfaced with the statisticalprocess control analysis unit and connected to the processing controllerin a feedback manner.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention may be understood by reference to the followingdescription taken in conjunction with the accompanying drawings, inwhich like reference numerals identify like elements, and in which:

FIG. 1 illustrates one embodiment of the present invention;

FIG. 2 illustrates a flowchart representation of one method of updatinga manufacturing model;

FIG. 3 illustrates a flowchart representation of the methods taught bythe present invention;

FIG. 4 illustrates a flowchart representation of a more detaileddepiction of the step of performing fault detection on a run-to-runcontroller described in FIG. 3;

FIG. 5 illustrates a flowchart representation of a more detaileddepiction of the step of performing process controller performancemonitoring described in FIG. 4; and

FIG. 6 illustrates a block diagram representation of the apparatustaught by the present invention.

While the invention is susceptible to various modifications andalternative forms, specific embodiments thereof have been shown by wayof example in the drawings and are herein described in detail. It shouldbe understood, however, that the description herein of specificembodiments is not intended to limit the invention to the particularforms disclosed, but on the contrary, the intention is to cover allmodifications, equivalents, and alternatives falling within the spiritand scope of the invention as defined by the appended claims.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

Illustrative embodiments of the invention are described below. In theinterest of clarity, not all features of an actual implementation aredescribed in this specification. It will of course be appreciated thatin the development of any such actual embodiment, numerousimplementation-specific decisions must be made to achieve thedevelopers' specific goals, such as compliance with system-related andbusiness-related constraints, which will vary from one implementation toanother. Moreover, it will be appreciated that such a development effortmight be complex and time-consuming, but would nevertheless be a routineundertaking for those of ordinary skill in the art having the benefit ofthis disclosure.

There are many discrete processes that are involved in semiconductormanufacturing. Many times, semiconductor devices are stepped throughmultiple manufacturing process tools. As semiconductor devices areprocessed through manufacturing tools, production data, or manufacturingdata, is generated. The production data can be used to perform faultdetection analysis that can lead to improved manufacturing results.Overlay process is an important group of process steps in semiconductormanufacturing. In particular, overlay process involves measuringmisalignment errors between semiconductor layers during manufacturingprocesses. Improvements in the overlay process could result insubstantial enhancements, in terms of quality and efficiency, insemiconductor manufacturing processes. The present invention provides amethod of acquiring production data and performing fault analysis on aprocess controller, such as a run-to-run controller, in response to theacquired production data.

Turning now to FIG. 1, one embodiment of the present invention isillustrated. In one embodiment, semiconductor products 105, such assemiconductor wafers are processed on processing tools 110, 112 using aplurality of control input signals on a line 120. In one embodiment, thecontrol input signals on the line 120 are sent to the processing tools110, 112 from a computer system 130 via machine interfaces 115, 117. Inone embodiment, the first and second machine interfaces 115, 117 arelocated outside the processing tools 110, 112. In an alternativeembodiment, the first and second machine interfaces 115, 117 are locatedwithin the processing tools 110, 112.

In one embodiment, the computer system 130 sends control input signalson a line 120 to the first and second machine interfaces 115, 117. Thecomputer system 130 employs a manufacturing model 140 to generate thecontrol input signals on the line 120. In one embodiment, themanufacturing model 140 defines a process script and input control thatimplement a particular manufacturing process. The control input signalson a line 120 that are intended for processing tool A 110 are receivedand processed by the first machine interface 115. The control inputsignals on a line 120 that are intended for processing tool B 112 arereceived and processed by the second machine interface 117. Examples ofthe processing tools 110, 112 used in semiconductor manufacturingprocesses are steppers.

For processing tools such as steppers, the control inputs, on the line120, that are used to operate the processing tools 110, 112 include anx-translation signal, a y-translation signal, an x-expansion wafer scalesignal, a y-expansion wafer scale signal, a reticle magnificationsignal, and a reticle rotation signal. Generally, errors associated withthe reticle magnification signal and the reticle rotation signal relateto one particular exposure process on the surface of the wafer beingprocessed in the exposure tool. One of the primary features taught bythe present invention is a method of detecting and organizing fault datafor semiconductor manufacturing processes.

For photolithography processes, when a process step in a processing tool110, 112 is concluded, the semiconductor product 105 or wafer that isbeing processed is examined in a review station. One such review stationis a KLA review station. One set of data derived from the operation ofthe review station is a quantitative measure of the amount ofmisregistration that was caused by the previous exposure process. In oneembodiment, the amount of misregistration relates to the misalignment inthe process that occurred between two layers of a semiconductor wafer.In one embodiment, the amount of misregistration that occurred can beattributed to the control inputs for a particular exposure process. Thecontrol inputs generally affect the accuracy of the process stepsperformed by the processing tools 110, 112 on the semiconductor wafer.Modifications of the control inputs can be utilized to improve theperformance of the process steps employed in the manufacturing tool.Many times, the errors that are found in the processed semiconductorproducts 105 can be correlated to a particular fault analysis andcorrective actions can be taken to reduce the errors.

Turning now to FIG. 2, a flowchart representation of one embodiment of aprocess for updating the manufacturing model 140 is illustrated. In oneembodiment, the manufacturing model 140 that is used by a processcontroller, such as an Advanced Process Control (APC) system, isdefined, as described in block 210 of FIG. 2. Once the manufacturingmodel 140 is defined, a manufacturing run of semiconductor devices, suchas semiconductor wafers, is performed, as described in block 220 of FIG.2. When the manufacturing run of semiconductor wafers is complete, a setof production data is collected, including measuring a plurality ofwafer parameters, as described in block 230 of FIG. 2. The waferparameters include misalignment and misregistration errors duringphotolithography processes. The wafer parameters also include measuringpost-polish thickness errors during a polishing process.

In one embodiment, the production data is used to update themanufacturing model 140, which is used by the process controller tomodify control input signals for a subsequent manufacturing run ofsemiconductor wafers, as described in block 240 of FIG. 2. Therun-to-run controller then implements the next manufacturing run ofsemiconductor wafers and the feedback process is repeated as illustratedin FIG. 2. In general, an ideal manufacturing model 140 would result inrandom production errors that are distributed evenly over aGuassian-type error curve. However, due to the non-ideal characteristicsof manufacturing models 140, non-random errors could occur. An errorbias can develop within the manufacturing model resulting in consistenterrors during semiconductor manufacturing.

In some manufacturing processes, there are over 300 process steps thatare defined by a manufacturing model 140 and are implemented on asemiconductor wafer. A change in any one of the process steps can affectother related process steps such that the manufacturing model 140 thatdefines the process steps can become inaccurate. In some cases a chainreaction in the production of semiconductor wafers caused by a change ina particular process step can cause the manufacturing model 140 to nolonger describe the process steps well, causing errors in production. Inother words, a bias is created in the manufacturing model 140 such thatnow there is a consistent defect in the processed semiconductor wafers.For example, if the original manufacturing model was designed togenerate semiconductor wafers with zero overlay error, a bias in themanufacturing model 140 can cause a 10 micro-meter misalignment error inevery semiconductor wafer that is processed under the control of themanufacturing model 140. In other words, there is noise in themanufacturing system that is implementing the manufacturing model 140that causes non-random errors that are outside a normal Guassian errorcurve.

Furthermore, the aging of a manufacturing model 140 may causedegradation of output products that are manufactured within amanufacturing model 140 structure. In other words, gradually over time,a manufacturing model 140 can change its prior behavior. One example ofaging of a manufacturing model 140 is degradation of lamps in anexposure tool. Implementation of the methods taught by the presentinvention can reduce the effects of aging of manufacturing models 140.The present invention teaches a method of implementing a statisticalprocess control analysis method for reducing the bias and noise inmanufacturing systems.

In one embodiment, statistical process control (SPC) is a method ofmonitoring, controlling, and, ideally, improving a process throughstatistical analysis. In one embodiment, SPC analysis is comprised offour main steps. The main steps of SPC analysis include measuring theprocess, reducing variances in the process to make the process moreconsistent, monitoring the process, and improving the process to produceits best value. In real-time SPC, which can be used for run-to-runcontrol applications in one embodiment, data is collected from the mostrecently finished manufacturing run of semiconductor wafers before thenext manufacturing run of semiconductor wafers is processed. Steps aretaken to ensure that the quality of the processed semiconductor wafersare as consistent as possible from one manufacturing run to another.Generally, SPC analysis rules dictate that causes of errors discoveredduring one manufacturing run of semiconductor wafers must be correctedbefore the next manufacturing run of semiconductor wafers is performed.

Turning now to FIG. 3, a flowchart depiction of one embodiment of thepresent invention is illustrated. In one embodiment, a manufacturingmodel 140 that is used by a process controller, such as an AdvancedProcess Control (APC) system, is defined, as described in block 310 ofFIG. 3. Once the manufacturing model 140 is defined, a manufacturing runof semiconductor devices, such as semiconductor wafers, is performed, asdescribed in block 320 of FIG. 3. When a manufacturing run ofsemiconductor wafers is completed, a fault detection analysis isperformed on the process controller, as described in block 330 of FIG.3. In one embodiment, the fault detection analysis is performed on aprocess controller that is a run-to-run controller. A more detaileddepiction of the step of performing fault detection analysis, describedin block 330 of FIG. 3, is illustrated in FIG. 4.

Turning now to FIG. 4, after a manufacturing run of semiconductor wafersis completed, the corresponding production data is acquired, asdescribed in block 410 of FIG. 4. The production data that is acquiredincludes misalignment errors, misregistration errors, critical dimensionerrors, polishing thickness errors, and the like. Once production datais acquired, a process controller performance monitoring step isperformed, as described in block 420 of FIG. 4. A more detaileddepiction of the step of performing process controller performancemonitoring described in block 420 of FIG. 4, is illustrated in FIG. 5.

Turning now to FIG. 5, one embodiment of performing process controllerperformance monitoring is illustrated. In one embodiment, amanufacturing model 140 that is used by a process controller is defined,as described in block 510 of FIG. 5. Subsequently, semiconductor wafersare processed using the manufacturing model, as described in block 520of FIG. 5. Once a set of semiconductor wafers is processed,manufacturing parameters, such as production data, are measured, asdescribed in block 530 of FIG. 5. The manufacturing parameters that aremeasured include misalignment errors, misregistration errors, criticaldimension errors, and polishing thickness error. In one embodiment,manufacturing parameters are measured using metrology tools.

Once the manufacturing parameters are measured, modification data iscalculated for modifying parameters defined by the manufacturing model140, as described in block 540 of FIG. 5. Concurrently, SPC analysis,which is described above, is performed after processing of semiconductorwafers, as described in block 550 of FIG. 5. In one embodiment, whileperforming SPC analysis, a prediction is made regarding the expectedprocess behavior for a particular manufacturing model 140. Afterprocessing a set of semiconductor wafers, the results from analysis ofthe semiconductor wafers is compared with the predicted processbehavior, as described in block 560 of FIG. 5. In other words, ajudgment is made regarding how different the actual results from aprocessing step are from a set of predicted results for that processingstep. In one embodiment, standard SPC calculation methods that are knownto those skilled in the art, and having the benefit of the presentdisclosure, are employed for SPC analysis for the present invention.

The difference between the predicted process results and the actualprocess results is used to determine whether the manufacturing model 140should be modified for the next manufacturing run of semiconductorwafers, thereby performing fault detection upon a run-to-run controller.Using results obtained by measuring manufacturing parameters andperforming SPC analysis, the manufacturing model 140 is then modified tobe used for subsequent manufacturing processes, as described in block570 of FIG. 5. The modification of the manufacturing model 140 describedin block 570 completes the step of performing process controlperformance monitoring that is described in block 420 of FIG. 4.

Turning back to FIG. 4, once the manufacturing model 140 is modified,the modified manufacturing model 140 is implemented into the processcontroller that controls subsequent processing of semiconductor devices,as described in block 430 of FIG. 4. Modification factors needed to makemodification to the control input signals on the line 120 arecalculated, as described in block 440 of FIG. 4. The completion of thecalculations described in block 440 of FIG. 4 completes the step ofperforming fault detection analysis on the process controller that isdescribed in block 330 of FIG. 3. Turning back to FIG. 3, oncecalculations for modifying control input signals are made, the controlinput signal on the line 120 are modified to be used for a subsequentmanufacturing run of semiconductor wafers, as described in block 340 ofFIG. 3.

Turning now to FIG. 6, one embodiment of the apparatus for implementingthe principles taught by the present invention is illustrated. An innerfeedback loop is created between a processing controller 610, aprocessing tool 620, a metrology tool 630, and a control modificationdata calculation unit 640. The processing controller 610 is interfacedwith the processing tool 620. In one embodiment, the processingcontroller 610 calculates and sends control input signals that controlthe function of the processing tool 620. The processing tool 620 isinterfaced with the metrology tool 630, which performs measurement ofmanufacturing parameters on semiconductor wafers that are processed bythe processing tool 620.

The metrology tool 630 is interfaced with the control modification datacalculation unit 640. The control modification data calculation unit 640uses data provided by the metrology tool 630 to perform calculations forthe modification of control input signals that are generated by theprocessing controller 610. In one embodiment, the control modificationdata calculation unit 640 is a computer program that is interfaced withthe processing controller 610. Data from the control modification datacalculation unit 640 is utilized by the processing controller 610 tomodify control input signals that are sent to the processing tool 620for a subsequent processing of semiconductor wafers.

Concurrently, an outer feedback is created between the processingcontroller 610, the processing tool 620, the predictor function 650, theSPC analysis unit 660, and the results versus prediction analysis unit670. The predictor function 650 is interfaced with the processingcontroller 610 and predicts an expected result of a manufacturing run ofsemiconductor wafers, based on the control input signals generated bythe processing controller 610. In one embodiment, the predictor function650 is a computer program and is located within a manufacturing model140. Data from the processed semiconductor wafers is used by the SPCanalysis unit 660 to perform SPC analysis. In one embodiment, the SPCanalysis unit 660 is a computer program that is interfaced with themanufacturing model 140. The results versus prediction analysis unit 670calculates the differences between the predicted results of amanufacturing run of semiconductor wafers and the actual results of amanufacturing run of semiconductor wafers. In one embodiment, theresults versus prediction analysis unit 670 is a computer program. Thedata calculated by the results versus prediction analysis unit 670 isused by the processing controller 610 to modify control input signalsfor a subsequent manufacturing run of semiconductor wafers that isperformed by the processing tool 620. The principles taught by thepresent invention can be implemented into other types of manufacturingframeworks.

The principles taught by the present invention can be implemented in anAdvanced Process Control (APC) Framework. The APC is a preferredplatform from which to implement the overlay control strategy taught bythe present invention. In some embodiments, the APC can be afactory-wide software system, therefore, the control strategies taughtby the present invention can be applied to virtually any of thesemiconductor manufacturing tools on the factory floor. The APCframework also allows for remote access and monitoring of the processperformance. Furthermore, by utilizing the APC framework, data storagecan be more convenient, more flexible, and less expensive than localdrives. The APC platform allows for more sophisticated types of controlbecause it provides a significant amount of flexibility in writing thenecessary software code.

Deployment of the control strategy taught by the present invention ontothe APC framework could require a number of software components. Inaddition to components within the APC framework, a computer script iswritten for each of the semiconductor manufacturing tools involved inthe control system. When a semiconductor manufacturing tool in thecontrol system is started in the semiconductor manufacturing fab, itgenerally calls upon a script to initiate the action that is required bythe process controller, such as the overlay controller. The controlmethods are generally defined and performed in these scripts. Thedevelopment of these scripts can comprise a significant portion of thedevelopment of a control system.

The particular embodiments disclosed above are illustrative only, as theinvention may be modified and practiced in different but equivalentmanners apparent to those skilled in the art having the benefit of theteachings herein. Furthermore, no limitations are intended to thedetails of construction or design herein shown, other than as describedin the claims below. It is therefore evident that the particularembodiments disclosed above may be altered or modified and all suchvariations are considered within the scope and spirit of the invention.Accordingly, the protection sought herein is as set forth in the claimsbelow.

What is claimed:
 1. A method for monitoring controller performance usingstatistical process control analysis, comprising: defining amanufacturing model; performing a processing run of semiconductordevices as defined by said manufacturing model and implemented by aprocess controller; performing a fault detection analysis on saidprocess controller to reduce a bias in said manufacturing model,performing said fault detection analysis comprises performing saidstatistical process control analysis prior to performing a resultsversus prediction analysis; and updating at least one control inputsignal generated by said process controller in response to said faultdetection analysis.
 2. The method described in claim 1, wherein defininga manufacturing model further comprises defining a manufacturing modelthat describes a photolithography process for semiconductor devices. 3.The method described in claim 1, wherein defining a manufacturing modelfurther comprises defining a manufacturing model describes an etchingprocess for semiconductor devices.
 4. The method described in claim 1,wherein defining a manufacturing model further comprises defining amanufacturing model describes a deposition process for semiconductordevices.
 5. The method described in claim 1, wherein defining amanufacturing model further comprises defining a manufacturing modeldescribes an implantation process for semiconductor devices.
 6. Themethod described in claim 1, wherein defining a manufacturing modelfurther comprises defining a manufacturing model describes achemical-mechanical polishing process for semiconductor devices.
 7. Themethod described in claim 1, wherein performing a fault detectionanalysis on said process controller further comprises: acquiringproduction data; performing process controller performance monitoringusing said production data; modifying said manufacturing model inresponse to said process controller performance monitoring; andimplementing said modified manufacturing model in said processcontroller.
 8. The method described in claim 7, wherein acquiringproduction data further comprises acquiring metrology data using ametrology tool.
 9. The method described in claim 7, wherein performingprocess controller performance monitoring using said production datafurther comprises: measuring manufacturing parameters; calculatingmodification data based upon said manufacturing parameters; performingstatistical process control analysis; performing results versusprediction analysis based upon said statistical process controlanalysis; and modifying said manufacturing model based upon saidcalculated modification data and said results versus predictionanalysis.
 10. The method described in claim 9, wherein measuringmanufacturing parameters further comprises measuring metrology datausing a metrology tool.
 11. The method described in claim 9, whereinmeasuring metrology data further comprises measuring a misalignmenterror on a semiconductor wafer.
 12. The method described in claim 9,wherein measuring metrology data further comprises measuring amisregistration on a semiconductor wafer.
 13. The method described inclaim 9, wherein measuring metrology data further comprises measuringpolish thickness error on a semiconductor wafer.
 14. The methoddescribed in claim 9, wherein performing results versus predictionanalysis further comprises comparing a predicted manufacturing processbehavior to a result of a measured manufacturing process.
 15. Anapparatus for monitoring controller performance using statisticalprocess control analysis, comprising: a processing controller adapted toperform a fault detection analysis to reduce a bias in a manufacturingmodel performing a fault detection analysis on said process controllerto reduce a bias in said manufacturing model, said fault detectionanalysis comprising performing said statistical process control analysisprior to performing a results versus prediction analysis; a processingtool coupled with said processing controller; a metrology toolinterfaced with said processing tool; a control modification datacalculation unit interfaced with said metrology and connected to saidprocessing controller in a feedback manner; a predictor functioninterfaced with said processing controller; a statistical processcontrol analysis unit interfaced with said predictor function and saidprocessing tool; and a results versus prediction analysis unitinterfaced with said statistical process control analysis unit andconnected to said processing controller in a feedback manner.
 16. Theapparatus described in claim 15, wherein said processing controller is arun-to-run controller.
 17. The apparatus described in claim 15, whereinsaid processing controller an automatic process control (APC) system.18. The apparatus described in claim 15, wherein said controlmodification data calculation unit is a computer software programintegrated into said processing controller.
 19. The apparatus describedin claim 15, wherein said predictor function is a computer softwareprogram integrated into a manufacturing model.
 20. The apparatusdescribed in claim 15, wherein said statistical process control analysisunit is a computer software program.
 21. The apparatus described inclaim 15, wherein said results versus prediction analysis unit is acomputer software program.
 22. An apparatus for monitoring controllerperformance using statistical process control analysis, comprising:means for defining a manufacturing model; means for performing aprocessing run of semiconductor devices as defined by said manufacturingmodel and implemented by a process controller; means for performing afault detection analysis on said process controller to reduce a bias insaid manufacturing model, performing said fault detection analysiscomprises performing said statistical process control analysis prior toperforming a results versus prediction analysis; and means for updatingat least one control input signal generated by said process controllerin response to said fault detection analysis.
 23. A computer readableprogram storage device encoded with instructions that, when executed bya computer, performs a method for monitoring controller performanceusing statistical process control analysis, comprising: defining amanufacturing model; performing a processing run of semiconductordevices as defined by said manufacturing model and implemented by aprocess controller; performing a fault detection analysis on saidprocess controller to reduce a bias in said manufacturing model,performing said fault detection analysis comprises performing saidstatistical process control analysis prior to performing a resultsversus prediction analysis; and updating at least one control inputsignal generated by said process controller in response to said faultdetection analysis.
 24. The computer readable program storage deviceencoded with instructions that, when executed by a computer, performsthe method described in claim 23, wherein defining a manufacturing modelfurther comprises defining a manufacturing model that describes aphotolithography process for semiconductor devices.
 25. The computerreadable program storage device encoded with instructions that, whenexecuted by a computer, performs the method described in claim 23,wherein defining a manufacturing model further comprises defining amanufacturing model describes an etching process for semiconductordevices.
 26. The computer readable program storage device encoded withinstructions that, when executed by a computer, performs the methoddescribed in claim 23, wherein defining a manufacturing model furthercomprises defining a manufacturing model describes a deposition processfor semiconductor devices.
 27. The computer readable program storagedevice encoded with instructions that, when executed by a computer,performs the method described in claim 23, wherein defining amanufacturing model further comprises defining a manufacturing modeldescribes an implantation process for semiconductor devices.
 28. Thecomputer readable program storage device encoded with instructions that,when executed by a computer, performs the method described in claim 23,wherein defining a manufacturing model further comprises defining amanufacturing model describes a chemical-mechanical polishing processfor semiconductor devices.
 29. The computer readable program storagedevice encoded with instructions that, when executed by a computer,performs the method described in claim 23, wherein performing a faultdetection analysis on said process controller further comprise:acquiring production data; performing process controller performancemonitoring using said production data; modifying said manufacturingmodel in response to said process controller performance monitoring; andimplementing said modified manufacturing model in said processcontroller.
 30. The computer readable program storage device encodedwith instructions that, when executed by a computer, performs the methoddescribed in claim 29, wherein acquiring production data furthercomprises acquiring metrology data using a metrology tool.
 31. Thecomputer readable program storage device encoded with instructions that,when executed by a computer, performs the method described in claim 29,wherein performing process controller performance monitoring using saidproduction data further comprises: measuring manufacturing parameters;calculating modification data based upon said manufacturing parameters;performing statistical process control analysis; performing resultsversus prediction analysis based upon said statistical process controlanalysis; and modifying said manufacturing model based upon saidcalculated modification data and said results versus predictionanalysis.
 32. The computer readable program storage device encoded withinstructions that, when executed by a computer, performs the methoddescribed in claim 31, wherein measuring manufacturing parametersfurther comprises measuring metrology data using a metrology tool. 33.The computer readable program storage device encoded with instructionsthat, when executed by a computer, performs the method described inclaim 31, wherein measuring metrology data further comprises measuring amisalignment error on a semiconductor wafer.
 34. The computer readableprogram storage device encoded with instructions that, when executed bya computer, performs the method described in claim 31, wherein measuringmetrology data further comprises measuring a misregistration on asemiconductor wafer.
 35. The computer readable program storage deviceencoded with instructions that, when executed by a computer, performsthe method described in claim 31, wherein measuring metrology datafurther comprises measuring polish thickness error on a semiconductorwafer.
 36. The computer readable program storage device encoded withinstructions that, when executed by a computer, performs the methoddescribed in claim 31, wherein performing results versus predictionanalysis further comprises comparing a predicted manufacturing processbehavior to a result of a measured manufacturing process.